How to Build an Analytics Engineering Case Study

An analytics engineering case study should let a reviewer answer five questions quickly: What decision did the work support? What data did you start with? What did each model represent? How did you test it? What changed because the result became available?

The goal is not to make a small project sound like a production platform. The goal is to show how you reasoned, what you built, and which claims the evidence supports.

Start with the decision, not the tools

Open with the business question in one sentence. “Why did recognized revenue fall in March?” is stronger than “I built a modern data stack.” Then name the intended user and the decision they needed to make.

State the scope honestly. A portfolio project can use a static public dataset and still show production judgment. Say what the data can establish, what it cannot establish, and which assumptions stand in for missing source-system context.

Declare the grain before showing the diagram

For every business-facing model, write the sentence “one row represents …”. A revenue mart might contain one row per recognized invoice line per day. A retention model might contain one row per account per cohort month. This gives the reviewer a concrete way to judge joins, keys, and measures.

Next, show the shortest useful lineage:

1. Source tables and the fields you trusted.
2. Staging models that rename, cast, and deduplicate.
3. Intermediate models that apply reusable business logic.
4. Marts that answer the stated question at a declared grain.

A small diagram is enough. The text should explain why each boundary exists.

Document decisions a reviewer can challenge

Choose two or three decisions that had plausible alternatives. Examples include using order date instead of payment date, assigning refunds to the original transaction month, selecting a snapshot strategy, or keeping an expensive model incremental.

For each decision, record the options, the evidence available, the choice, and the consequence. “Used an incremental model for performance” is vague. “Rebuilt 14 days on each run because refunds can arrive up to 10 days late” can be reviewed and tested.

Show tests tied to failure modes

A list of generic tests says little. Connect each important test to the error it prevents:

• A uniqueness test protects the declared grain.
• A relationship test catches orphaned foreign keys.
• An accepted-values test protects a controlled business state.
• A reconciliation query compares the mart with a known source total.
• A freshness check identifies late upstream data before a dashboard refresh.

Include one failure you encountered and how you diagnosed it. That usually reveals more judgment than a screenshot of a green test run.

Report only verified outcomes

Separate measured results from expected benefits. If you timed the query before and after a change, report the method, data volume, and result. If you did not observe a business outcome, do not invent one. Write that the model now makes a decision possible and explain how a team would measure adoption or impact.

Useful evidence includes query runtime, tested row counts, reconciliation differences, build duration, documented metric definitions, and a screenshot of the final decision surface. Avoid unsupported percentages and unnamed “industry studies.”

A review-ready outline

1. Business question: the decision, user, and scope.
2. Data contract: sources, grain, keys, and known limitations.
3. Model design: lineage and the responsibility of each layer.
4. Decisions: alternatives considered and why you chose one.
5. Quality: tests, reconciliation, and a failure you debugged.
6. Result: verified output and claims you cannot yet make.
7. Reproduction: setup steps, commands, and sample output.

Use the project README template to draft the written version, then compare it with the revenue investigation take-home for an example of evidence boundaries.

Final check

A reviewer should be able to reproduce the core path, identify the grain, trace a metric to its source, and distinguish observed results from assumptions. If those four things are clear, the case study is doing its job.